I am currently trying to find a solution to a transportation problem. I have a table with the travel times from 30 customers to each other. The goal is to use dynamic program to go from this table with direct durations to a table with shortest duration possible (via possible subtours between pairs of customers)
I have no clue how to solve this. The question states "Let π·π,π,π be the shortest (in duration) path between customer π and π, using only nodes 0 to π as intermediaries."
Is there someone who can help me with this?
Thank you in advance. :-)
to use only nodes 0 to k as intermediates.
structure(list(ID = 1:30, X1 = c(0L, 98L, 132L, 245L, 17L, 69L,
139L, 112L, 207L, 35L, 249L, 43L, 215L, 62L, 152L, 237L, 59L,
119L, 45L, 59L, 23L, 12L, 16L, 66L, 177L, 31L, 118L, 165L, 117L,
193L), X2 = c(37L, 0L, 74L, 176L, 91L, 111L, 143L, 208L, 202L,
40L, 172L, 101L, 163L, 51L, 220L, 180L, 48L, 98L, 45L, 62L, 91L,
9L, 50L, 145L, 198L, 48L, 3L, 163L, 159L, 176L), X3 = c(118L,
10L, 0L, 138L, 108L, 115L, 221L, 274L, 231L, 89L, 197L, 36L,
175L, 93L, 174L, 184L, 13L, 109L, 109L, 22L, 172L, 91L, 12L,
149L, 140L, 108L, 8L, 133L, 168L, 139L), X4 = c(252L, 137L, 112L,
0L, 248L, 276L, 373L, 350L, 353L, 137L, 31L, 186L, 49L, 170L,
325L, 81L, 117L, 184L, 175L, 185L, 279L, 182L, 120L, 205L, 130L,
132L, 133L, 226L, 158L, 67L), X5 = c(40L, 93L, 118L, 188L, 0L,
38L, 147L, 106L, 208L, 46L, 228L, 26L, 208L, 54L, 163L, 212L,
81L, 49L, 45L, 84L, 12L, 82L, 114L, 81L, 173L, 67L, 52L, 63L,
146L, 156L), X6 = c(54L, 117L, 143L, 296L, 74L, 0L, 78L, 97L,
218L, 52L, 239L, 47L, 267L, 111L, 216L, 269L, 167L, 91L, 105L,
99L, 16L, 117L, 95L, 15L, 264L, 136L, 148L, 100L, 145L, 242L),
X7 = c(143L, 145L, 208L, 302L, 130L, 86L, 0L, 84L, 308L,
151L, 348L, 102L, 305L, 139L, 254L, 272L, 156L, 182L, 87L,
214L, 110L, 82L, 145L, 59L, 283L, 98L, 171L, 192L, 211L,
228L), X8 = c(142L, 195L, 238L, 336L, 161L, 80L, 2L, 0L,
289L, 135L, 342L, 134L, 307L, 169L, 197L, 347L, 261L, 143L,
128L, 243L, 52L, 128L, 169L, 97L, 318L, 214L, 229L, 225L,
287L, 288L), X9 = c(230L, 238L, 217L, 264L, 229L, 154L, 299L,
269L, 0L, 210L, 335L, 204L, 342L, 260L, 25L, 258L, 264L,
76L, 170L, 222L, 126L, 193L, 272L, 203L, 218L, 186L, 214L,
120L, 240L, 207L), X10 = c(2L, 30L, 34L, 186L, 75L, 96L,
161L, 179L, 244L, 0L, 199L, 17L, 181L, 59L, 240L, 178L, 101L,
117L, 1L, 48L, 79L, 72L, 24L, 60L, 236L, 5L, 32L, 151L, 203L,
133L), X11 = c(243L, 191L, 114L, 38L, 164L, 211L, 295L, 370L,
350L, 211L, 0L, 174L, 14L, 166L, 278L, 164L, 165L, 223L,
252L, 129L, 201L, 196L, 201L, 271L, 98L, 199L, 209L, 219L,
168L, 62L), X12 = c(22L, 14L, 64L, 164L, 68L, 113L, 117L,
115L, 208L, 8L, 239L, 0L, 204L, 36L, 230L, 217L, 37L, 62L,
41L, 30L, 64L, 25L, 22L, 60L, 150L, 30L, 90L, 109L, 92L,
178L), X13 = c(224L, 168L, 120L, 23L, 213L, 310L, 324L, 377L,
366L, 212L, 54L, 178L, 0L, 207L, 316L, 105L, 117L, 266L,
235L, 106L, 231L, 186L, 147L, 223L, 109L, 182L, 211L, 183L,
130L, 132L), X14 = c(66L, 22L, 77L, 204L, 23L, 143L, 93L,
171L, 259L, 37L, 182L, 68L, 218L, 0L, 216L, 186L, 56L, 139L,
65L, 44L, 118L, 64L, 19L, 112L, 183L, 14L, 40L, 142L, 96L,
131L), X15 = c(167L, 236L, 263L, 275L, 187L, 189L, 212L,
282L, 9L, 181L, 309L, 231L, 340L, 238L, 0L, 207L, 216L, 97L,
204L, 237L, 120L, 160L, 217L, 188L, 258L, 203L, 183L, 106L,
174L, 267L), X16 = c(173L, 196L, 161L, 152L, 168L, 268L,
322L, 280L, 265L, 230L, 133L, 160L, 102L, 209L, 193L, 0L,
162L, 168L, 228L, 208L, 283L, 159L, 195L, 270L, 14L, 233L,
176L, 172L, 48L, 38L), X17 = c(70L, 44L, 13L, 184L, 90L,
186L, 194L, 267L, 257L, 72L, 179L, 38L, 107L, 88L, 177L,
172L, 0L, 93L, 71L, 14L, 167L, 117L, 37L, 146L, 116L, 35L,
62L, 83L, 93L, 129L), X18 = c(115L, 128L, 153L, 196L, 78L,
109L, 147L, 185L, 103L, 138L, 218L, 123L, 200L, 85L, 91L,
178L, 86L, 0L, 35L, 125L, 67L, 68L, 142L, 42L, 202L, 84L,
81L, 91L, 87L, 210L), X19 = c(36L, 104L, 71L, 166L, 16L,
99L, 88L, 133L, 200L, 35L, 246L, 2L, 263L, 89L, 144L, 229L,
47L, 103L, 0L, 31L, 114L, 32L, 44L, 106L, 219L, 43L, 92L,
119L, 150L, 103L), X20 = c(74L, 66L, 22L, 101L, 76L, 101L,
194L, 211L, 170L, 21L, 179L, 105L, 197L, 36L, 180L, 204L,
48L, 71L, 59L, 0L, 178L, 32L, 58L, 125L, 180L, 46L, 16L,
77L, 130L, 75L), X21 = c(85L, 158L, 144L, 210L, 29L, 16L,
44L, 65L, 141L, 88L, 258L, 70L, 279L, 124L, 168L, 190L, 145L,
45L, 40L, 110L, 0L, 129L, 157L, 52L, 177L, 117L, 175L, 153L,
133L, 190L), X22 = c(25L, 44L, 124L, 236L, 62L, 39L, 108L,
149L, 175L, 18L, 177L, 16L, 259L, 44L, 163L, 179L, 74L, 110L,
28L, 104L, 110L, 0L, 97L, 123L, 216L, 5L, 112L, 170L, 153L,
196L), X23 = c(26L, 2L, 39L, 205L, 102L, 74L, 205L, 203L,
257L, 49L, 194L, 96L, 207L, 5L, 174L, 160L, 37L, 159L, 55L,
55L, 91L, 1L, 0L, 153L, 216L, 52L, 11L, 192L, 116L, 149L),
X24 = c(12L, 127L, 115L, 288L, 90L, 60L, 73L, 79L, 194L,
87L, 203L, 81L, 257L, 108L, 188L, 263L, 185L, 90L, 25L, 177L,
51L, 85L, 93L, 0L, 234L, 114L, 111L, 72L, 131L, 153L), X25 = c(194L,
120L, 191L, 143L, 212L, 253L, 281L, 318L, 291L, 160L, 108L,
174L, 94L, 188L, 203L, 43L, 127L, 153L, 204L, 162L, 170L,
237L, 194L, 251L, 0L, 237L, 122L, 149L, 75L, 104L), X26 = c(10L,
58L, 22L, 171L, 101L, 131L, 144L, 154L, 177L, 5L, 222L, 48L,
220L, 59L, 192L, 193L, 9L, 134L, 23L, 80L, 75L, 19L, 19L,
132L, 197L, 0L, 57L, 146L, 163L, 154L), X27 = c(104L, 58L,
15L, 175L, 46L, 178L, 206L, 185L, 199L, 44L, 191L, 115L,
211L, 33L, 200L, 148L, 25L, 104L, 47L, 8L, 154L, 35L, 41L,
106L, 159L, 14L, 0L, 171L, 88L, 87L), X28 = c(80L, 137L,
93L, 225L, 76L, 138L, 245L, 195L, 102L, 130L, 197L, 112L,
169L, 163L, 68L, 83L, 108L, 50L, 125L, 108L, 139L, 106L,
131L, 111L, 136L, 179L, 150L, 0L, 103L, 151L), X29 = c(170L,
113L, 89L, 120L, 138L, 172L, 224L, 263L, 233L, 155L, 170L,
132L, 194L, 133L, 160L, 99L, 106L, 86L, 154L, 90L, 132L,
158L, 142L, 209L, 55L, 127L, 118L, 93L, 0L, 67L), X30 = c(197L,
170L, 155L, 66L, 178L, 193L, 226L, 303L, 213L, 161L, 104L,
94L, 140L, 138L, 241L, 29L, 102L, 131L, 108L, 129L, 182L,
167L, 167L, 198L, 83L, 154L, 89L, 106L, 87L, 0L)), class = "data.frame", row.names = c(NA,
-30L))
Following up on my comment to use the igraph package, below is a way to accomplish what is described.
library(igraph)
m <- data.frame(X1 = c(0L, 98L, 132L, 245L, 17L, 69L, 139L, 112L, 207L, 35L, 249L, 43L, 215L, 62L, 152L, 237L, 59L, 119L, 45L, 59L, 23L, 12L, 16L, 66L, 177L, 31L, 118L, 165L, 117L, 193L),
X2 = c(37L, 0L, 74L, 176L, 91L, 111L, 143L, 208L, 202L, 40L, 172L, 101L, 163L, 51L, 220L, 180L, 48L, 98L, 45L, 62L, 91L, 9L, 50L, 145L, 198L, 48L, 3L, 163L, 159L, 176L),
X3 = c(118L, 10L, 0L, 138L, 108L, 115L, 221L, 274L, 231L, 89L, 197L, 36L, 175L, 93L, 174L, 184L, 13L, 109L, 109L, 22L, 172L, 91L, 12L, 149L, 140L, 108L, 8L, 133L, 168L, 139L),
X4 = c(252L, 137L, 112L, 0L, 248L, 276L, 373L, 350L, 353L, 137L, 31L, 186L, 49L, 170L, 325L, 81L, 117L, 184L, 175L, 185L, 279L, 182L, 120L, 205L, 130L, 132L, 133L, 226L, 158L, 67L),
X5 = c(40L, 93L, 118L, 188L, 0L, 38L, 147L, 106L, 208L, 46L, 228L, 26L, 208L, 54L, 163L, 212L, 81L, 49L, 45L, 84L, 12L, 82L, 114L, 81L, 173L, 67L, 52L, 63L, 146L, 156L),
X6 = c(54L, 117L, 143L, 296L, 74L, 0L, 78L, 97L, 218L, 52L, 239L, 47L, 267L, 111L, 216L, 269L, 167L, 91L, 105L, 99L, 16L, 117L, 95L, 15L, 264L, 136L, 148L, 100L, 145L, 242L),
X7 = c(143L, 145L, 208L, 302L, 130L, 86L, 0L, 84L, 308L, 151L, 348L, 102L, 305L, 139L, 254L, 272L, 156L, 182L, 87L, 214L, 110L, 82L, 145L, 59L, 283L, 98L, 171L, 192L, 211L, 228L),
X8 = c(142L, 195L, 238L, 336L, 161L, 80L, 2L, 0L, 289L, 135L, 342L, 134L, 307L, 169L, 197L, 347L, 261L, 143L, 128L, 243L, 52L, 128L, 169L, 97L, 318L, 214L, 229L, 225L, 287L, 288L),
X9 = c(230L, 238L, 217L, 264L, 229L, 154L, 299L, 269L, 0L, 210L, 335L, 204L, 342L, 260L, 25L, 258L, 264L, 76L, 170L, 222L, 126L, 193L, 272L, 203L, 218L, 186L, 214L, 120L, 240L, 207L),
X10 = c(2L, 30L, 34L, 186L, 75L, 96L, 161L, 179L, 244L, 0L, 199L, 17L, 181L, 59L, 240L, 178L, 101L, 117L, 1L, 48L, 79L, 72L, 24L, 60L, 236L, 5L, 32L, 151L, 203L, 133L),
X11 = c(243L, 191L, 114L, 38L, 164L, 211L, 295L, 370L, 350L, 211L, 0L, 174L, 14L, 166L, 278L, 164L, 165L, 223L, 252L, 129L, 201L, 196L, 201L, 271L, 98L, 199L, 209L, 219L, 168L, 62L),
X12 = c(22L, 14L, 64L, 164L, 68L, 113L, 117L, 115L, 208L, 8L, 239L, 0L, 204L, 36L, 230L, 217L, 37L, 62L, 41L, 30L, 64L, 25L, 22L, 60L, 150L, 30L, 90L, 109L, 92L, 178L),
X13 = c(224L, 168L, 120L, 23L, 213L, 310L, 324L, 377L, 366L, 212L, 54L, 178L, 0L, 207L, 316L, 105L, 117L, 266L, 235L, 106L, 231L, 186L, 147L, 223L, 109L, 182L, 211L, 183L, 130L, 132L),
X14 = c(66L, 22L, 77L, 204L, 23L, 143L, 93L, 171L, 259L, 37L, 182L, 68L, 218L, 0L, 216L, 186L, 56L, 139L, 65L, 44L, 118L, 64L, 19L, 112L, 183L, 14L, 40L, 142L, 96L, 131L),
X15 = c(167L, 236L, 263L, 275L, 187L, 189L, 212L, 282L, 9L, 181L, 309L, 231L, 340L, 238L, 0L, 207L, 216L, 97L, 204L, 237L, 120L, 160L, 217L, 188L, 258L, 203L, 183L, 106L, 174L, 267L),
X16 = c(173L, 196L, 161L, 152L, 168L, 268L, 322L, 280L, 265L, 230L, 133L, 160L, 102L, 209L, 193L, 0L, 162L, 168L, 228L, 208L, 283L, 159L, 195L, 270L, 14L, 233L, 176L, 172L, 48L, 38L),
X17 = c(70L, 44L, 13L, 184L, 90L, 186L, 194L, 267L, 257L, 72L, 179L, 38L, 107L, 88L, 177L, 172L, 0L, 93L, 71L, 14L, 167L, 117L, 37L, 146L, 116L, 35L, 62L, 83L, 93L, 129L),
X18 = c(115L, 128L, 153L, 196L, 78L, 109L, 147L, 185L, 103L, 138L, 218L, 123L, 200L, 85L, 91L, 178L, 86L, 0L, 35L, 125L, 67L, 68L, 142L, 42L, 202L, 84L, 81L, 91L, 87L, 210L),
X19 = c(36L, 104L, 71L, 166L, 16L, 99L, 88L, 133L, 200L, 35L, 246L, 2L, 263L, 89L, 144L, 229L, 47L, 103L, 0L, 31L, 114L, 32L, 44L, 106L, 219L, 43L, 92L, 119L, 150L, 103L),
X20 = c(74L, 66L, 22L, 101L, 76L, 101L, 194L, 211L, 170L, 21L, 179L, 105L, 197L, 36L, 180L, 204L, 48L, 71L, 59L, 0L, 178L, 32L, 58L, 125L, 180L, 46L, 16L, 77L, 130L, 75L),
X21 = c(85L, 158L, 144L, 210L, 29L, 16L, 44L, 65L, 141L, 88L, 258L, 70L, 279L, 124L, 168L, 190L, 145L, 45L, 40L, 110L, 0L, 129L, 157L, 52L, 177L, 117L, 175L, 153L, 133L, 190L),
X22 = c(25L, 44L, 124L, 236L, 62L, 39L, 108L, 149L, 175L, 18L, 177L, 16L, 259L, 44L, 163L, 179L, 74L, 110L, 28L, 104L, 110L, 0L, 97L, 123L, 216L, 5L, 112L, 170L, 153L, 196L),
X23 = c(26L, 2L, 39L, 205L, 102L, 74L, 205L, 203L, 257L, 49L, 194L, 96L, 207L, 5L, 174L, 160L, 37L, 159L, 55L, 55L, 91L, 1L, 0L, 153L, 216L, 52L, 11L, 192L, 116L, 149L),
X24 = c(12L, 127L, 115L, 288L, 90L, 60L, 73L, 79L, 194L, 87L, 203L, 81L, 257L, 108L, 188L, 263L, 185L, 90L, 25L, 177L, 51L, 85L, 93L, 0L, 234L, 114L, 111L, 72L, 131L, 153L),
X25 = c(194L, 120L, 191L, 143L, 212L, 253L, 281L, 318L, 291L, 160L, 108L, 174L, 94L, 188L, 203L, 43L, 127L, 153L, 204L, 162L, 170L, 237L, 194L, 251L, 0L, 237L, 122L, 149L, 75L, 104L),
X26 = c(10L, 58L, 22L, 171L, 101L, 131L, 144L, 154L, 177L, 5L, 222L, 48L, 220L, 59L, 192L, 193L, 9L, 134L, 23L, 80L, 75L, 19L, 19L, 132L, 197L, 0L, 57L, 146L, 163L, 154L),
X27 = c(104L, 58L, 15L, 175L, 46L, 178L, 206L, 185L, 199L, 44L, 191L, 115L, 211L, 33L, 200L, 148L, 25L, 104L, 47L, 8L, 154L, 35L, 41L, 106L, 159L, 14L, 0L, 171L, 88L, 87L),
X28 = c(80L, 137L, 93L, 225L, 76L, 138L, 245L, 195L, 102L, 130L, 197L, 112L, 169L, 163L, 68L, 83L, 108L, 50L, 125L, 108L, 139L, 106L, 131L, 111L, 136L, 179L, 150L, 0L, 103L, 151L),
X29 = c(170L, 113L, 89L, 120L, 138L, 172L, 224L, 263L, 233L, 155L, 170L, 132L, 194L, 133L, 160L, 99L, 106L, 86L, 154L, 90L, 132L, 158L, 142L, 209L, 55L, 127L, 118L, 93L, 0L, 67L),
X30 = c(197L, 170L, 155L, 66L, 178L, 193L, 226L, 303L, 213L, 161L, 104L, 94L, 140L, 138L, 241L, 29L, 102L, 131L, 108L, 129L, 182L, 167L, 167L, 198L, 83L, 154L, 89L, 106L, 87L, 0L))
system.time({
g <- graph_from_adjacency_matrix(as.matrix(m), "directed", TRUE, FALSE)
mOpt <- shortest.paths(g, V(g), V(g), mode = "out")
})
#> user system elapsed
#> 0 0 0
mOpt[1:10, 1:10]
#> X1 X2 X3 X4 X5 X6 X7 X8 X9 X10
#> X1 0 21 25 133 36 27 71 73 123 2
#> X2 18 0 10 122 40 45 89 91 127 17
#> X3 36 18 0 112 58 63 107 109 145 27
#> X4 130 112 117 0 152 157 201 203 239 129
#> X5 17 36 40 148 0 44 88 81 127 17
#> X6 39 48 52 160 28 0 86 68 142 41
#> X7 67 88 92 200 56 60 0 2 170 69
#> X8 88 109 113 221 77 81 84 0 191 90
#> X9 157 165 170 250 140 157 208 193 0 154
#> X10 22 19 23 131 34 49 92 94 121 0
mOptPaths <- lapply(V(g), function(from) all_shortest_paths(g, from = from, mode = "out"))
# optimal path from 1 to 2
(path_1_2 <- as.integer(mOptPaths[[1]]$res[[2]]))
#> [1] 1 10 26 22 2
# distances for path from 1 to 2
m[matrix(c(head(path_1_2, -1), path_1_2[-1]), ncol = 2)]
#> [1] 2 5 5 9
Related
I'm trying to make a heatmap of locations using latitude and longitude. My map works but the points in an area are being grouped together too much. What I want is something like what I have but with the heat traced much more granularly so that you can see smaller differences between places.
This is the relevant code:
bbox <- c(left = -79.1, bottom = 38, right = -71.8, top = 41.5)
coords.map <- get_stamenmap(bbox, zoom = 9, maptype = "toner-lite")
coords.map <- ggmap(coords.map, extent="device", legend="none")
coords.map <- coords.map + stat_density2d(data=recruits2,
aes(x=MailingLong, y=MailingLat, fill=..level.., alpha=..level..),
bins = 20, geom = "polygon")
coords.map <- coords.map + scale_fill_gradientn(colours=rev(brewer.pal(20, "Spectral")))
coords.map <- coords.map + theme_bw()
coords.map
This is what I'm getting
Edit: Here is the first 200 rows of recruits from dput
structure(list(MailingLong = c(-77.024196799, -77.003914977,
-76.800101376, -77.116797729, -77.018188811, -77.120614114, -76.975844627,
-77.147034967, -77.089368856, -76.97864858, -76.844030329, -76.9639802739999,
-77.257533391, -76.822314419, -80.0753704429999, -76.551298185,
-76.767346001, -78.7422733669999, -77.2918568269999, -74.252185664,
-77.035277127, -76.957338864, -77.403195991, -77.6319986669999,
-73.944649177, -76.5599475129999, -73.571443862, -76.989599958,
-76.8866457089999, -76.8900467889999, -77.027862935, -76.763716223,
-74.035259712, -77.075101833, -76.9065139969999, -77.009444624,
-76.991265512, -76.998362508, -77.0138546809999, -76.92147388,
-76.937957008, -73.897316132, -77.2021361069999, -76.9865863379999,
-77.057626187, -77.2698420199999, -73.962312531, -77.061793597,
-74.118483634, -73.8812691919999, -84.1753095879999, -76.849177244,
-76.9494415629999, -77.011200646, -77.12310071, -76.8884595649999,
-77.0061180499999, -77.017453829, -76.946342907, -76.980566735,
-76.923624123, -77.010469798, -77.02638151, -76.918852741, -76.9224746959999,
-87.802292575, -76.813914677, -73.9355411179999, -77.0086792399999,
-76.9349190729999, -77.020158344, -76.9487103719999, -73.846362546,
-77.015177632, -80.255301241, -77.428318142, -77.0007635819999,
-76.929540264, -76.947718204, -76.93487089, -77.019630846, -77.018265258,
-73.893035738, -76.887356744, -74.746807223, -76.825205743, -76.8953029259999,
-76.962229153, -77.0435782929999, -73.844867129, -76.926558429,
-76.8879371259999, -73.8547164669999, -76.920915296, -81.556675863,
-77.088045767, -76.8971086939999, -73.2218500929999, -77.283520263,
-77.0097035049999, -73.995325534, -82.7629230599999, -76.952207819,
-77.012592416, -77.0260711729999, -76.942692268, -76.981824075,
-73.261707374, -77.0867976709999, -76.904238182, -77.172019128,
-76.9965300979999, -77.0255964739999, -76.984859179, -76.9812736649999,
-77.081601215, -76.759961416, -74.6169999639999, -77.043506263,
-77.202603773, -76.949909772, -76.9530278129999, -76.7527797119999,
-75.053749344, -76.746774219, -71.778088926, -77.02110899, -76.854547506,
-77.084631879, -76.997437527, -76.901572162, -73.870879122, -76.878514023,
-74.1344631809999, -74.318660444, -82.7075784359999, -74.7182968279999,
-76.797062055, -77.5707813399999, -77.6124343819999, -75.5027960319999,
-76.987749881, -76.9821815619999, -75.828326588, -79.783059322,
-76.9499001519999, -80.259390617, -77.117644909, -77.2343587779999,
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I try do define the model for my test and training dataset. But I get the following Error:
Error in eval(predvars, data, env) : object 'avg_rating' not found
But all of my datasets have the "avg_rating"
This is my code
lm_model <- train(avg_rating ~., data = trainingindex,method = "lm",na.action = na.omit, preProcess = c("scale", "center"),trControl = trainControl(method = "none"))
structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 13L, 14L,
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31L, 32L, 33L, 35L, 36L), .Dim = c(30L, 1L), .Dimnames = list(
NULL, "Resample1"))
I have two datasets with ellipses centroids coordinates (X & Y), Areas, Major and Minor axes (and other variables). My aim is to calculate the overlap between the groups of ellipses and plot them.
The main question is regarding calculations of overlapping regions.
For plotting I would use draw.ellipse function but I'm open to other alternatives.
The two datasets are very similar, here's a subset of one of the two datasets.
dput(slide0[,2:5])
structure(list(Area = c(15453600L, 89L, 151L, 80L, 72L, 126L,
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136.908, 118.826, 96.091, 65.258, 80.337, 81.307, 99.708, 79.43,
110.613, 133.293, 119.548, 120.531, 145.672, 152.551, 112.212,
99.511, 174.431, 124.639, 96.213, 96.519, 73.313, 95.089, 107.768,
83.267, 61.609, 75.427, 71.286, 71.432, 46.537, 45.476, 43.022,
62.453, 57.119, 70.371, 51.357, 61.375, 83.333, 82.426, 79.96,
116.067, 105.707, 87.325, 112.895, 109.095, 117.281, 111.854,
148.683, 133.125, 104.845, 127.963, 137.671, 138.911, 140.825,
81.808, 96.727, 105.228, 97.221, 111.427, 132.558, 128.643, 81.949,
80.36, 92.705, 118.899, 144.306, 74.203, 143.779, 101.706, 97.2,
112.246, 128.369, 138.031, 120.625, 89.94, 106.357, 128.086,
123.375, 131.397, 150.083, 132.829, 145.618, 91.547, 116.659,
106.016, 112.1, 130.752, 116.714, 109.788, 123.153, 88.089, 87.811,
76.714, 105.028, 116.762, 110.763, 86.912, 124.126, 113.262,
100.559, 82.238, 100.933, 84.739, 88.28, 103.925, 123.255, 131.902,
101.19, 91.973, 133.035, 145.464, 138.7, 114.074, 141.062, 149.383,
153.36, 154.899, 157.742, 150.806, 136.886, 148.42, 161.966,
128.024, 154.977, 156.037, 183.664, 134.484, 143.471, 174.431,
112.766, 84.021, 109.633, 102.055, 122.228, 119.169, 126.669,
92.72, 84.557, 109.622, 115.52, 98.305, 100.016, 86.705, 88.524,
93.91, 121.433, 115.186, 138.775, 112.171, 139.945, 111.459,
137.648, 148.56, 151.7, 147.475, 130.412, 150.611, 144.699, 124.879,
127.666, 133.582, 128.419, 113.841, 143.732, 129.874, 147.084,
125.908, 98.857, 128.482, 135.695, 98.767, 94.26, 96.024, 68.207,
96.417, 122.438, 122.296, 105.178, 113.808, 123.345, 120.631,
77.151, 102.319, 118.283, 111.146, 103.658, 117.19, 119.658,
131.902, 141.754, 136.67, 130.594, 123.112, 99.02, 128.873, 143.095,
92.819, 113.151, 127.487, 113.512, 121.085, 147.4, 142.526, 151.355,
134.403, 146.5, 138.858, 155.043, 132.473, 126.667, 68.433, 70.066,
147.763, 146.615, 128.263, 130.767, 121.531, 125.728, 159.721,
132.185, 106.326, 162.808, 150.12, 133, 153.806, 138.598, 138.126,
120.59, 121.269, 139.493, 152.304, 71.971, 82.636, 98.034, 82.251,
102.697, 122.014, 97.708, 75.286, 52.676, 98.066, 94.866, 95.919,
82.12, 109.942, 91.606, 86.238, 125.225, 105.902, 119.013, 126.057,
124.857, 109.714, 120.282, 149.981, 81.801, 70.83, 142.661, 149.28,
139.454, 137.897, 119.04, 140.558, 148.685, 140.614, 164.547,
149.968, 136.695, 153.167, 171.521, 148.609, 151.662, 140.758,
139.857, 138.801, 116.912, 97.34, 61.571, 80.522, 88.731, 98.472,
74.622, 69.777, 82.164, 103.745, 99.347, 93.855, 102.203, 94.188,
98.527, 68.627, 63.579, 70.838, 87.632, 102.745, 92.405, 88.791,
70.331, 92.744, 106.656, 79.491, 61.176, 78.644, 98.818, 87.208,
98.686, 91.403, 75.044, 94.814, 87.394, 102.771, 90.769, 80.698,
58.295, 94.543, 81.545, 82.067, 95.034, 85.513, 89.924, 98.911,
68.598, 95.787, 71.672, 76.026, 79.773, 87.8, 71.2, 95.482, 87.57,
119.214, 77.196, 75.767, 62.02, 76.571, 89.089, 79.957, 75.821,
80.648, 85.476, 93.469, 78.106, 82.495, 83.729, 95.271, 85.641,
89.673, 86.904, 74.656, 105.274, 91.341, 106.678, 118.634, 100.529,
62.436, 72.042, 110.141, 78.265, 66.938, 87.006, 73.267, 94.289,
87.381, 97.522, 83.391, 147.455, 76.407, 94.759, 112.042, 104.347,
122.056, 104.762, 129.169, 125.432, 120.088, 84.685, 94.495,
92.923, 128.421, 120.352, 118.516, 106.805, 92.767, 98.958, 109.758,
121.721, 103.106, 123.208, 134.923, 69.045, 78.149, 60.458, 161.341,
174.319, 140.344, 157.43, 145.615, 66.009, 105.366, 112.884,
81.873, 109.462, 124.463, 106.618, 92.798, 111.902, 109.031,
110.728, 122.747, 146.079, 95.329, 71.645, 71.396, 105.551, 125.577,
108.667, 96.525, 129.236, 112.384, 108.867, 124.959, 115.832,
84.398, 109.375, 86.628, 106.676, 118.657, 113.338, 120.794,
127.88, 133.464, 98.284, 114.703, 92.94, 102.952, 107.424, 99.102,
81.107, 110.479, 137.737, 163.38, 151.75, 152.721, 133.849, 139.288,
117.071, 150.302, 142.243, 131.826, 127.75, 114.162, 118.68,
118.971, 102.493, 130.689, 159.573, 169.638, 178.302, 166.951,
156.924, 101.371, 108.549, 91.515, 93.934, 110.864, 111.93, 131.61,
109.916, 105.618, 116.5, 101.277, 114.187, 119.904, 105.36, 127.161,
154.102, 60.025, 49.68, 136.403, 138.291, 61.87, 94.677, 95.404,
98.725, 98.675, 93.288, 88.75, 78.517, 67.692, 90.513, 97.99,
88.561, 90.403, 80.934, 81.885, 97.8, 86.376, 85.306, 75.233,
111.649, 118.692, 103.755, 91.964, 98.381, 117.338, 164.294,
86.323, 58.865, 66.819, 116.067, 121.83, 127.481, 134.677, 112.538,
96.477, 98.662, 96.006, 81.853, 97.816, 106.143, 89.558, 114.297,
139.307, 134.272, 130.72, 130.2, 88.085, 108.413, 142.852, 133.355,
121.06, 89.391, 95.952, 96.659, 133.213, 125, 100.683, 92.884,
106.593, 107.779, 99.766, 103.653, 62.968, 116.287, 126.256,
135.006, 128.476, 132.255, 124.1, 149.011, 127.481, 136.932,
144.322, 113.102, 129.422, 122.393, 115.367, 111.584, 144.991,
116.725, 147.935, 135.571, 137.036, 171.311, 129.237, 128.368,
144.16, 115.514, 123.665, 129.138, 145.752, 118.607, 129.668,
113.674, 117.143, 108.503, 96.714, 137.52, 107.203, 111.435,
90.057, 119.367, 110.799, 101.931, 113.069, 120.825, 119.478,
106.346, 118.9, 161.491, 102.136, 93.389, 102.111, 96.429, 120.19
), Min = c(25L, 85L, 88L, 103L, 99L, 77L, 70L, 70L, 68L, 95L,
75L, 109L, 77L, 117L, 69L, 87L, 65L, 47L, 47L, 72L, 112L, 95L,
117L, 77L, 85L, 68L, 94L, 83L, 130L, 58L, 83L, 49L, 92L, 53L,
57L, 72L, 72L, 49L, 83L, 86L, 102L, 93L, 112L, 76L, 69L, 89L,
77L, 69L, 128L, 131L, 117L, 65L, 67L, 72L, 86L, 67L, 78L, 64L,
73L, 78L, 82L, 56L, 77L, 79L, 71L, 81L, 57L, 92L, 71L, 87L, 70L,
73L, 72L, 58L, 71L, 57L, 68L, 90L, 72L, 79L, 77L, 64L, 96L, 90L,
100L, 101L, 88L, 77L, 70L, 85L, 106L, 72L, 63L, 66L, 76L, 72L,
71L, 57L, 67L, 46L, 56L, 78L, 82L, 80L, 87L, 111L, 100L, 107L,
54L, 84L, 51L, 62L, 74L, 85L, 88L, 68L, 50L, 80L, 58L, 66L, 74L,
84L, 83L, 105L, 80L, 86L, 62L, 57L, 68L, 89L, 56L, 78L, 90L,
85L, 103L, 116L, 80L, 85L, 85L, 94L, 84L, 88L, 78L, 102L, 85L,
64L, 58L, 90L, 100L, 112L, 126L, 127L, 134L, 83L, 120L, 117L,
131L, 119L, 80L, 78L, 109L, 84L, 73L, 116L, 116L, 122L, 130L,
149L, 110L, 136L, 130L, 126L, 104L, 134L, 151L, 128L, 95L, 114L,
84L, 108L, 92L, 90L, 90L, 107L, 98L, 124L, 84L, 88L, 103L, 89L,
87L, 87L, 90L, 74L, 85L, 85L, 99L, 109L, 99L, 107L, 95L, 102L,
108L, 98L, 83L, 61L, 110L, 98L, 79L, 93L, 94L, 62L, 111L, 96L,
126L, 119L, 154L, 123L, 123L, 122L, 129L, 107L, 113L, 138L, 76L,
84L, 90L, 80L, 93L, 93L, 90L, 109L, 55L, 105L, 94L, 99L, 95L,
107L, 103L, 107L, 107L, 75L, 99L, 95L, 113L, 112L, 130L, 122L,
135L, 65L, 79L, 104L, 87L, 94L, 71L, 85L, 113L, 106L, 103L, 80L,
85L, 97L, 58L, 76L, 98L, 50L, 65L, 72L, 81L, 88L, 106L, 107L,
99L, 85L, 77L, 46L, 60L, 61L, 80L, 59L, 78L, 83L, 96L, 100L,
109L, 119L, 85L, 77L, 131L, 77L, 67L, 65L, 50L, 72L, 74L, 56L,
45L, 62L, 64L, 65L, 39L, 39L, 38L, 53L, 50L, 57L, 43L, 56L, 66L,
62L, 64L, 91L, 90L, 73L, 93L, 82L, 89L, 81L, 121L, 104L, 69L,
107L, 118L, 111L, 120L, 65L, 78L, 87L, 77L, 96L, 98L, 108L, 64L,
69L, 75L, 107L, 98L, 42L, 132L, 75L, 62L, 62L, 101L, 110L, 106L,
76L, 96L, 97L, 92L, 94L, 123L, 105L, 122L, 62L, 87L, 87L, 82L,
94L, 101L, 90L, 97L, 68L, 44L, 56L, 82L, 74L, 86L, 66L, 105L,
87L, 67L, 59L, 70L, 58L, 54L, 77L, 104L, 98L, 85L, 65L, 96L,
89L, 87L, 95L, 101L, 123L, 117L, 136L, 106L, 128L, 90L, 117L,
104L, 91L, 132L, 132L, 140L, 100L, 108L, 129L, 61L, 64L, 67L,
80L, 91L, 80L, 75L, 63L, 61L, 85L, 67L, 67L, 73L, 69L, 59L, 70L,
105L, 88L, 114L, 72L, 112L, 79L, 104L, 129L, 112L, 120L, 95L,
119L, 122L, 92L, 104L, 113L, 103L, 82L, 106L, 98L, 125L, 83L,
63L, 100L, 107L, 83L, 63L, 73L, 53L, 73L, 98L, 91L, 84L, 88L,
81L, 86L, 54L, 80L, 84L, 82L, 53L, 81L, 68L, 85L, 88L, 84L, 87L,
80L, 80L, 92L, 99L, 58L, 80L, 89L, 88L, 89L, 94L, 100L, 113L,
110L, 103L, 113L, 122L, 114L, 95L, 59L, 56L, 114L, 117L, 110L,
112L, 92L, 103L, 131L, 100L, 85L, 118L, 116L, 109L, 129L, 102L,
121L, 98L, 92L, 108L, 132L, 50L, 48L, 64L, 52L, 58L, 86L, 71L,
49L, 42L, 67L, 80L, 68L, 55L, 69L, 70L, 62L, 96L, 81L, 75L, 79L,
86L, 77L, 77L, 90L, 59L, 47L, 90L, 116L, 102L, 97L, 101L, 112L,
114L, 131L, 132L, 115L, 99L, 102L, 136L, 114L, 126L, 106L, 108L,
93L, 88L, 72L, 46L, 66L, 56L, 75L, 54L, 49L, 63L, 73L, 77L, 79L,
84L, 74L, 68L, 51L, 51L, 52L, 57L, 88L, 71L, 61L, 55L, 75L, 75L,
57L, 47L, 59L, 74L, 70L, 68L, 69L, 52L, 69L, 65L, 77L, 52L, 65L,
37L, 66L, 60L, 57L, 79L, 66L, 62L, 72L, 42L, 66L, 44L, 52L, 63L,
72L, 44L, 78L, 72L, 91L, 59L, 59L, 53L, 51L, 58L, 59L, 59L, 59L,
70L, 64L, 54L, 71L, 58L, 71L, 67L, 76L, 54L, 53L, 81L, 61L, 90L,
85L, 63L, 40L, 50L, 78L, 48L, 43L, 51L, 50L, 67L, 68L, 57L, 57L,
57L, 50L, 71L, 81L, 82L, 88L, 81L, 96L, 100L, 78L, 57L, 71L,
72L, 104L, 89L, 88L, 52L, 59L, 76L, 86L, 111L, 78L, 102L, 105L,
51L, 59L, 53L, 125L, 140L, 106L, 129L, 116L, 40L, 54L, 73L, 52L,
82L, 84L, 75L, 73L, 95L, 89L, 100L, 104L, 117L, 79L, 48L, 43L,
89L, 78L, 69L, 62L, 108L, 83L, 85L, 97L, 88L, 59L, 88L, 66L,
88L, 97L, 95L, 103L, 110L, 101L, 85L, 94L, 73L, 71L, 79L, 76L,
52L, 88L, 90L, 101L, 117L, 122L, 117L, 104L, 89L, 108L, 94L,
102L, 113L, 78L, 88L, 94L, 85L, 105L, 136L, 131L, 143L, 131L,
127L, 52L, 76L, 58L, 68L, 88L, 79L, 89L, 82L, 76L, 99L, 58L,
89L, 89L, 68L, 84L, 95L, 45L, 43L, 107L, 122L, 40L, 75L, 74L,
73L, 82L, 69L, 67L, 65L, 51L, 75L, 73L, 73L, 76L, 56L, 68L, 81L,
65L, 70L, 55L, 84L, 90L, 89L, 65L, 76L, 82L, 96L, 58L, 38L, 47L,
94L, 100L, 106L, 114L, 87L, 70L, 70L, 83L, 61L, 84L, 89L, 72L,
87L, 111L, 113L, 98L, 102L, 67L, 93L, 128L, 102L, 89L, 57L, 75L,
67L, 89L, 93L, 77L, 59L, 91L, 89L, 76L, 70L, 50L, 85L, 100L,
102L, 100L, 95L, 99L, 114L, 114L, 116L, 109L, 95L, 105L, 104L,
100L, 91L, 116L, 92L, 114L, 114L, 114L, 134L, 100L, 106L, 112L,
87L, 99L, 104L, 112L, 97L, 105L, 85L, 92L, 85L, 75L, 98L, 59L,
80L, 66L, 83L, 79L, 80L, 86L, 97L, 91L, 75L, 95L, 109L, 72L,
71L, 78L, 79L, 105L), Max = c(255L, 124L, 117L, 140L, 123L, 124L,
167L, 157L, 149L, 152L, 145L, 137L, 129L, 180L, 157L, 117L, 129L,
110L, 248L, 157L, 162L, 161L, 173L, 158L, 129L, 140L, 157L, 144L,
189L, 122L, 133L, 98L, 178L, 159L, 131L, 127L, 140L, 110L, 139L,
159L, 174L, 153L, 148L, 110L, 114L, 141L, 157L, 142L, 162L, 187L,
179L, 122L, 133L, 161L, 160L, 136L, 133L, 114L, 120L, 137L, 126L,
142L, 114L, 128L, 116L, 137L, 123L, 147L, 150L, 131L, 142L, 126L,
144L, 103L, 125L, 185L, 173L, 133L, 157L, 152L, 141L, 178L, 153L,
130L, 142L, 154L, 146L, 143L, 154L, 118L, 202L, 131L, 131L, 125L,
134L, 127L, 115L, 119L, 120L, 193L, 203L, 209L, 173L, 250L, 184L,
183L, 204L, 174L, 217L, 180L, 187L, 149L, 144L, 216L, 192L, 161L,
132L, 229L, 152L, 163L, 125L, 155L, 127L, 153L, 129L, 152L, 172L,
223L, 163L, 166L, 202L, 148L, 212L, 162L, 175L, 197L, 124L, 156L,
148L, 126L, 230L, 146L, 192L, 167L, 152L, 129L, 163L, 189L, 148L,
194L, 201L, 195L, 195L, 152L, 188L, 186L, 242L, 162L, 224L, 186L,
255L, 195L, 161L, 227L, 210L, 210L, 198L, 214L, 248L, 180L, 238L,
245L, 200L, 205L, 252L, 179L, 139L, 189L, 197L, 177L, 252L, 202L,
150L, 163L, 201L, 204L, 151L, 133L, 133L, 154L, 125L, 178L, 129L,
124L, 152L, 117L, 144L, 166L, 149L, 154L, 173L, 152L, 201L, 161L,
134L, 235L, 175L, 138L, 183L, 152L, 141L, 111L, 172L, 171L, 190L,
173L, 182L, 202L, 159L, 199L, 234L, 169L, 166L, 218L, 154L, 146L,
142L, 153L, 180L, 156L, 137L, 149L, 108L, 217L, 159L, 171L, 169L,
181L, 186L, 154L, 193L, 222L, 209L, 144L, 198L, 185L, 226L, 243L,
197L, 138L, 189L, 153L, 255L, 162L, 221L, 170L, 184L, 178L, 180L,
197L, 185L, 194L, 182L, 151L, 197L, 158L, 115L, 143L, 167L, 176L,
164L, 193L, 196L, 198L, 141L, 117L, 140L, 124L, 138L, 144L, 217L,
225L, 159L, 153L, 200L, 241L, 166L, 136L, 254L, 186L, 148L, 190L,
173L, 145L, 183L, 155L, 119L, 112L, 81L, 86L, 59L, 68L, 50L,
80L, 74L, 95L, 71L, 67L, 108L, 126L, 130L, 141L, 145L, 123L,
177L, 168L, 195L, 193L, 204L, 179L, 209L, 190L, 169L, 201L, 171L,
143L, 130L, 133L, 142L, 139L, 210L, 161L, 118L, 104L, 137L, 134L,
253L, 185L, 165L, 161L, 180L, 212L, 194L, 186L, 156L, 122L, 126L,
218L, 204L, 255L, 204L, 169L, 207L, 161L, 157L, 143L, 170L, 196L,
154L, 140L, 174L, 158L, 192L, 138L, 142L, 210L, 171L, 133L, 166L,
148L, 158L, 127L, 196L, 160L, 178L, 140L, 153L, 186L, 122L, 150L,
188L, 242L, 252L, 156L, 183L, 190L, 222L, 194L, 248L, 183L, 237L,
193L, 255L, 171L, 234L, 205L, 255L, 192L, 196L, 245L, 217L, 131L,
157L, 192L, 181L, 201L, 228L, 146L, 155L, 217L, 217L, 156L, 169L,
151L, 138L, 155L, 158L, 174L, 199L, 193L, 185L, 181L, 195L, 179L,
218L, 208L, 177L, 240L, 210L, 210L, 167L, 171L, 192L, 196L, 205L,
203L, 183L, 191L, 224L, 177L, 209L, 128L, 195L, 130L, 87L, 142L,
175L, 202L, 144L, 221L, 243L, 190L, 144L, 134L, 167L, 173L, 254L,
189L, 225L, 232L, 245L, 234L, 209L, 187L, 139L, 214L, 237L, 162L,
163L, 218L, 171L, 184L, 241L, 220L, 242L, 175L, 231L, 190L, 194L,
169L, 172L, 93L, 135L, 225L, 203L, 167L, 169L, 167L, 163L, 219L,
184L, 183L, 242L, 193L, 160L, 201L, 245L, 157L, 179L, 186L, 203L,
193L, 135L, 178L, 181L, 142L, 255L, 168L, 137L, 128L, 94L, 143L,
117L, 184L, 138L, 175L, 150L, 128L, 186L, 177L, 226L, 229L, 206L,
171L, 225L, 240L, 157L, 130L, 224L, 203L, 209L, 240L, 152L, 247L,
205L, 161L, 235L, 188L, 183L, 218L, 249L, 206L, 185L, 235L, 204L,
227L, 203L, 200L, 90L, 115L, 174L, 159L, 159L, 123L, 141L, 154L,
137L, 129L, 135L, 122L, 207L, 121L, 112L, 152L, 159L, 135L, 134L,
181L, 117L, 149L, 166L, 172L, 87L, 111L, 128L, 137L, 152L, 117L,
125L, 145L, 145L, 152L, 190L, 106L, 97L, 154L, 150L, 123L, 133L,
121L, 178L, 156L, 127L, 159L, 125L, 112L, 118L, 129L, 146L, 121L,
109L, 168L, 108L, 110L, 82L, 147L, 158L, 111L, 111L, 124L, 124L,
155L, 154L, 113L, 143L, 153L, 124L, 115L, 135L, 124L, 134L, 139L,
141L, 169L, 193L, 115L, 127L, 167L, 173L, 134L, 176L, 136L, 141L,
145L, 193L, 140L, 255L, 141L, 132L, 155L, 150L, 195L, 145L, 175L,
174L, 244L, 156L, 136L, 140L, 179L, 193L, 176L, 255L, 171L, 141L,
160L, 142L, 154L, 166L, 174L, 110L, 123L, 78L, 223L, 233L, 223L,
216L, 189L, 148L, 198L, 185L, 148L, 186L, 174L, 205L, 137L, 141L,
186L, 129L, 156L, 186L, 133L, 137L, 146L, 167L, 231L, 197L, 191L,
162L, 184L, 143L, 190L, 153L, 161L, 158L, 134L, 148L, 151L, 145L,
166L, 170L, 164L, 123L, 154L, 133L, 157L, 138L, 159L, 172L, 166L,
236L, 255L, 193L, 215L, 180L, 191L, 165L, 209L, 241L, 188L, 151L,
167L, 174L, 162L, 116L, 185L, 213L, 228L, 250L, 236L, 184L, 222L,
164L, 177L, 127L, 190L, 209L, 255L, 163L, 173L, 151L, 221L, 167L,
190L, 190L, 207L, 214L, 114L, 74L, 199L, 185L, 162L, 125L, 135L,
161L, 132L, 122L, 140L, 123L, 125L, 133L, 127L, 116L, 119L, 164L,
119L, 123L, 123L, 110L, 119L, 188L, 173L, 132L, 136L, 142L, 202L,
250L, 133L, 117L, 97L, 146L, 159L, 172L, 184L, 153L, 134L, 255L,
127L, 130L, 134L, 133L, 143L, 189L, 171L, 181L, 184L, 183L, 132L,
147L, 163L, 199L, 188L, 164L, 130L, 155L, 192L, 182L, 131L, 168L,
133L, 146L, 147L, 192L, 97L, 159L, 160L, 189L, 148L, 167L, 140L,
204L, 144L, 150L, 194L, 142L, 171L, 148L, 151L, 167L, 207L, 176L,
203L, 167L, 193L, 254L, 181L, 188L, 213L, 178L, 194L, 198L, 173L,
145L, 162L, 151L, 153L, 179L, 140L, 220L, 176L, 230L, 124L, 211L,
189L, 136L, 166L, 182L, 165L, 151L, 176L, 223L, 150L, 154L, 129L,
145L, 164L)), .Names = c("Area", "Mean", "Min", "Max"), class = "data.frame", row.names = c(NA,
-866L))
Goal
My data consists of numerous points which would be too much to represent as a scatter plot. I would like to plot a point density.
Specifically, I am wondering if there is a way to obtain something similar to this with ggplot2:
Current code
pEPNSABobs1<-ggplot(dataE, aes(comp1,mod1obs, group=1))+
geom_smooth(aes(color="chartreuse"), se=F, linetype="longdash", size=1)+
stat_smooth(data=dataE, fill="chartreuse", color="chartreuse4", linetype="blank")+
geom_smooth(data=dataS, aes(comp1,mod1obs, group=1, color="lightpink"), se=F, linetype="longdash", size=1)+
stat_smooth(data=dataS, fill="lightpink",color="lightpink4", linetype="blank")+
scale_colour_manual(name = 'Legend',
values =c('lightpink'='lightpink4','chartreuse'='chartreuse4'), labels = c('PIM','ABB'))+
scale_size_area() +
xlab("CDD0.year") +
ylab(expression(f[CDD0.year.obs]))+
labs(title='(A)')+
theme(plot.title = element_text(hjust = 0, vjust=1),axis.title = element_text(size = rel(1.2)), title=element_text(size = rel(1.2)),axis.text=element_text(size = rel(1.2)),
legend.text=element_text(size = rel(1.2)))
Data
DataE <- structure(list(comp1 = c(1338.2461, 1721.8119, 1878.2578, 1781.8827, 1813.2432, 1711.5277, 2033.0855, 1636.394, 1580.0748, 1834.4927, 2150.4177, 1790.7859, 1980.2718, 1610.0624, 2064.5809, 2002.82, 1652.7442, 2143.8216, 2015.1028, 2201.7947, 1610.4855, 1983.2706, 1979.9317, 2282.4141, 1763.288, 2204.3358, 1781.4969, 2114.2082, 1567.7841, 2089.6192, 1653.2401, 1709.9662, 2118.7251, 1843.5898, 1561.9472, 1839.0119, 2441.8013, 1684.3595, 1862.4287, 2043.3588, 2043.1502, 1999.5591, 1929.3686, 1897.746, 2073.494, 1345.488, 1622.3962, 1692.8681, 1847.7492, 1863.1212, 1759.8359, 2092.2891, 1671.9526, 1646.3103, 1918.8867, 2187.7568, 1833.4847, 2031.483, 1653.4428, 2118.2413, 2049.7907, 1705.1815, 2191.5739, 2054.9909, 2253.6989, 1664.6036, 2038.5609, 2021.5454, 2380.9635, 1788.6053, 2225.5046, 1834.5804, 2104.5817, 1523.2706, 2150.1426, 1722.6452, 1750.2867, 2142.4975, 1871.6772, 1562.7973, 1865.9236, 2424.8774, 1700.2864, 1850.6267, 2059.4913, 2047.4193, 2002.2637, 1941.7118, 1916.4346, 2081.8719, 1357.3087, 1645.5375, 1739.0249, 1984.4754, 2500.5373, 2126.0111, 2067.2634, 2163.0708, 2149.2563, 1939.6116, 1872.462, 2024.5612, 2158.432, 1732.0504, 2287.9286, 1640.2811, 2332.7904, 2475.8456, 2097.2091, 1942.9509, 2121.2583, 1899.7503, 2060.2692, 2459.2143, 2395.0327, 2280.1373, 2628.2383, 2118.6924, 2209.724, 2228.5074, 2008.7249, 1819.8529, 2144.0186, 2466.9707, 2479.4277, 2483.6576, 2403.0477, 2025.4349, 2150.5789, 2332.0055, 2115.4415, 1981.608, 1912.8366, 2296.8201, 1985.4613, 1875.0743, 1969.649, 2048.9522, 1900.5933, 1921.7856, 2066.8277, 1859.8671, 1698.0369, 1493.0678, 1910.5082, 2102.3289, 2048.3883, 1949.4451, 1838.7137, 2257.4143, 1958.3303, 1836.1803, 2138.1416, 2194.0097, 1919.8365, 2200.6554, 1829.6413, 2290.396, 2156.0543, 1854.5949, 2240.8976, 2060.3596, 2362.8724, 1794.141, 2183.8272, 2129.4498, 2517.4392, 1901.8863, 2440.1299, 2059.9263, 2291.6513, 1642.4418, 2281.0632, 1888.9796, 1931.7574, 2232.7776, 1953.4144, 1613.9024, 2000.765, 2485.617, 1774.7963, 1900.3834, 2192.5563, 2149.8866, 2090.371, 2076.542, 2050.3292, 2152.2821, 1482.7591, 1674.3366, 1905.3904, 1971.8098, 1596.7659, 1899.0917, 2400.7906, 2048.1242, 1968.4794, 2057.2929, 2053.9162, 1837.3839),
mod1obs = c(0.695830136555704, 1.06337206841913, 1.14861889402282, 1.02790421278896, 1.11775825622145, 1.43143032263757, 1.66436105688503, 1.79407632202943, 1.54246894155172, 1.47641083551176, 1.61115035184228, 1.68350958534902, 1.67052919588235, 1.86924870748066, 1.52139461422218, 1.7206336638354, 1.77255323580939, 1.67014367453142, 1.4828365451913, 1.30632542733184, 1.28066122419783, 1.36281355152317, 0.922105627695953, 0.485097355039981, 0.527121512464299, 0.810559323027329, 1.02715532531355, 1.14244699815587, 0.999551948498188, 0.951386076673739, 0.708742765512991, 0.591036273251615, 0.954269537513021, 1.33390119349384, 1.32340100204498, 1.48365670129187, 1.18354444471793, 1.17538278706292, 1.13658706470025, 1.09659824662134, 1.33968115088882, 0.97520848249232, 0.926838756160161, 0.916231908195398, 0.899325038616274, 1.16824595028327, 1.11899066863449, 1.11661928913426, 1.12284805168664, 1.05643303658747, 1.06710730526053, 1.22833157533542, 0.816937586646849, 1.00335563406519, 1.402274505934, 1.41743848783742, 1.14038690876476, 0.985366123273008, 0.843537703363177, 0.99753466530825, 0.956105174325841, 0.969042102699615, 1.0638789437522, 0.996797745632918, 0.941726356849215, 0.926215042532983, 1.03697021488655, 1.24561208170624, 0.903344544294698, 0.852585271143484, 1.14969403609893, 0.951461942556407, 0.969946352436395, 0.856006282102713, 1.11792476085488, 1.13412438712209, 1.15896430227978, 1.13984814117922, 1.26400232474479, 1.3157371290327, 1.10269366567886, 0.852330476973561, 1.06803837916353, 1.00555346861516, 1.07980330220743, 0.999413354748127, 0.835341074425715, 0.750343026273598, 0.750086242001393, 0.845059990359667, 1.01550610388101, 0.97183262908527, 1.02351377836664, 0.646201913922726, 0.832803589659996, 1.02112583534199, 1.05276036662667, 0.778087551043244, 1.04311980643046, 1.14374772863475, 1.26330982513545, 1.20248850820938, 1.03073057299644, 1.06049050327533, 0.834166914153454, 0.984789639654932, 1.08805486508421, 1.40007199005997, 1.33458208694833, 1.09447880143235, 1.32094094746787, 1.01033105751792, 1.15916733600421, 0.83814699368444, 0.922361964325012, 0.821150439904281, 0.757025921805821, 0.796454341025722, 0.753864630868797, 0.709679353471203, 0.947247272243527, 1.00883103860073, 0.954505648136825, 0.921522424138087, 0.845557836615405, 0.959332247443167, 0.726910705401073, 0.825894718073668, 0.849038625330317, 0.860196521211579, 0.88419468576954, 0.986401829980697, 0.844208566273473, 0.733125744746535, 0.718381697185048, 0.826648914954142, 0.787244473508777, 0.65512488225044, 0.752501264072651, 0.723469068584019, 0.743094728670719, 1.11846155736291, 1.08257557110011, 0.982979407982961, 0.885561974111481, 0.898359447667526, 0.770274340254794, 0.964105752093132, 1.419398413941, 1.06346164710011, 0.94787165982562, 1.21888848634176, 0.902929635142765, 0.924412982720593, 1.10282712434799, 0.899503164916735, 0.850644250882259, 1.01891899751745, 1.21897925575381, 1.26360292391086, 0.924187893834376, 0.930518507010402, 1.03052665863541, 1.15564902628815, 1.01660158016781, 0.904615715917612, 0.523182725557325, 0.625326640420025, 1.03256402151814, 1.19178281509474, 1.21239098183734, 1.00933857234806, 1.01170794284609, 1.02137919901009, 0.784528093252022, 0.97997559028248, 1.15073167836534, 1.09499252446889, 1.02629257850687, 0.865796462253095, 1.08500359034302, 1.14790809213261, 0.96317054146643, 1.1105434300635, 0.817447776121345, 0.888257155328381, 0.884423797252747, 0.935227482404514, 1.07740310874975, 1.02969991871418, 0.922959386699878, 0.642079018955661, 0.653388944491712, 0.597053974284795, 0.882728190259291, 0.918365607321804, 0.814327793136811, 1.00391967118938, 1.09246216046294, 1.07787732161824)),
row.names = c(2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 129L, 130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 139L, 140L, 141L, 142L, 143L, 144L, 145L, 146L, 147L, 148L, 149L, 150L, 151L, 152L, 153L, 154L, 155L, 156L, 157L, 158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 176L, 177L, 178L, 179L, 180L, 181L, 182L, 183L, 184L, 185L, 186L, 187L, 188L, 189L, 190L, 191L, 192L, 193L, 194L, 195L, 196L, 197L, 198L, 199L, 200L, 201L, 202L, 203L),
class = "data.frame")
and
DataS <- structure(list(comp1 = c(1800.2451, 1781.6087, 1737.2914, 1816.9749, 1643.348, 1438.5377, 1338.2461, 1721.8119, 1878.2578, 1781.8827, 1813.2432, 1711.5277, 2033.0855, 1636.394, 1580.0748, 1834.4927, 2150.4177, 1790.7859, 1980.2718, 1610.0624, 2064.5809, 2002.82, 1652.7442, 2143.8216, 2015.1028, 2201.7947, 1610.4855, 1983.2706, 1979.9317, 2282.4141, 1763.288, 2204.3358, 1781.4969, 2114.2082, 1567.7841, 2089.6192, 1653.2401, 1709.9662, 2118.7251, 1843.5898, 1561.9472, 1839.0119, 2441.8013, 1684.3595, 1862.4287, 2043.3588, 2043.1502, 1999.5591, 1929.3686, 1897.746, 2073.494, 1345.488, 1622.3962, 1692.8681, 1845.1739, 1661.4121, 1746.5679, 2225.7098, 1973.0317, 1877.0764, 1875.4714, 1996.5981, 1712.4015, 1715.2255, 2149.4741, 2303.7732, 1872.9362, 2279.4588, 1513.787, 2184.5552, 2361.3593, 2148.0699, 1978.381, 2008.029, 1955.9995, 1986.7266, 2079.5143, 1960.9757, 1809.0558, 2193.2401, 1729.3818, 1926.0217, 2157.6266, 1788.1556, 1475.6945, 1854.1782, 2090.9429, 2242.7276, 2120.1378, 1982.7239, 1860.3072, 1906.2126, 2115.5587, 1970.3582, 1809.3253, 1851.249, 2147.1908, 1774.673, 1816.4041, 1934.3119, 1901.6345, 1943.6281, 1793.2585, 1826.9192, 1727.9485, 1506.61, 1440.656, 1748.6882, 1956.9693, 1938.5561, 1850.1536, 1645.5995, 2086.7712, 1723.6074, 1805.0789, 2089.9296, 2139.9376, 1891.8347, 2158.9863, 1525.1479, 2132.2023, 2061.2398, 1707.3251, 2202.9106, 2017.0934, 2359.064, 1781.5352, 2081.9002, 2091.391, 2307.257, 1722.3419, 2200.4204, 1752.5609, 2110.2504, 1432.5841, 2121.8681, 1734.5244, 1912.6964, 2134.543, 1996.4716, 1720.3715, 1902.6841, 2402.3462, 1850.2001, 1919.6724, 2123.1945, 2177.1916, 2134.0072, 1969.5795, 1896.8108, 2132.3515, 1451.0973, 1612.5966, 1725.3021, 1581.0971, 2347.5604, 2483.6352, 2035.8553, 2426.3861, 1655.1293, 2382.9981, 2468.8676, 2256.6275, 2087.2235, 2124.0978, 2068.831, 2150.4903, 2262.8365, 2198.5672, 1968.5931, 2418.6407, 1953.0811, 2108.4554, 2361.584, 1979.866, 1741.6303, 2087.9856, 2286.7883, 2395.2738, 2270.3263, 2075.8854, 1874.2124, 2038.3772, 2245.1034, 2075.8632, 1946.548, 1955.202, 2355.13, 2011.3717, 1975.8039, 2099.3788, 2136.0128, 1909.835, 1965.8319, 1829.9619, 1630.4157, 1506.5606, 1897.0851, 2069.4124, 2024.9848),
mod1obs = c(1.13466310792768, 0.931923716274403, 1.48217365869915, 1.21838260146353, 1.79940276419435, 1.76643559356371, 2.45574670199418, 1.85759003858763, 1.52414873587958, 1.56924844587154, 1.75782289196183, 1.21854647206407, 1.67402271470746, 1.57661741696957, 1.54915950383801, 1.16767311295364, 1.51513098169545, 1.58721974127532, 1.48203349385421, 1.25150055063036, 1.55523168986921, 1.16757976105911, 1.06103912158879, 1.38682784393209, 1.26706181742404, 0.922200575100749, 1.15475122512565, 0.919460864722599, 0.622581998594566, 0.888857360932288, 0.561258461668506, 0.784221352878807, 1.05841796267749, 0.954913917366984, 0.864989456398651, 0.405901971360923, 0.437238711188011, 0.238832040897009, 0.398444779604082, 0.596968751447861, 0.780373761619182, 0.593964337126505, 0.674617006647725, 0.803014994811282, 0.914435218814932, 0.832419908437008, 0.870618364283852, 0.66205782789832, 0.668271801470194, 0.582218329504303, 0.551471500972662, 0.546862377936455, 0.782666777798501, 0.797397188682925, 0.233703554128143, 0.33078183414865, 0.477622787992685, 0.788352242940992, 0.655192745268702, 0.629025187982574, 0.606750329361944, 0.688876228035905, 0.811952779578203, 0.878151955673824, 0.666102340741867, 0.234485500963779, 0.377020837708222, 0.431015138976773, 0.599545478659815, 0.485341173599891, 0.54118137004067, 0.822700963102403, 0.680465320458669, 0.750819741408091, 0.914425077741122, 1.03163581838662, 0.750374660256944, 0.994337592683161, 1.00941746422589, 1.07118881485789, 1.2413361662074, 0.862210731330022, 0.807945428753615, 1.01206355371074, 1.1466781183792, 1.04520757430783, 0.906546865827641, 0.802138329712516, 0.927446849752543, 0.909606938810288, 0.726203964235328, 0.63174380758208, 0.827670510708034, 0.796140009519362, 0.908331184943455, 1.24437229369026, 1.01038032593664, 1.20106590210506, 1.47001915216196, 1.4216087908593, 1.19649249116061, 1.28729733432818, 1.050493946874, 0.490028901207541, 0.472897051757391, 0.474117219731324, 0.85500755809614, 1.16164758242536, 1.3257510136531, 1.28575789166575, 1.63477173725642, 2.45069111586777, 2.50502453325512, 1.70995863697504, 2.4729188484128, 2.65244156137921, 2.52288384634894, 2.91133218222026, 2.80631798084833, 2.85068631154525, 2.50623525545215, 2.39950652546403, 2.49930350794263, 2.47640980454363, 2.5743114316923, 2.27093700546959, 1.9784591153946, 1.794185941834, 1.59685624413893, 1.21577767933037, 1.1993875662254, 0.905547156548879, 0.791419465032495, 1.5777113878502, 1.46727507228871, 1.03812304747397, 1.04471285876448, 0.89791346550258, 1.00531560652384, 1.03963310517253, 1.55611837868102, 1.32499035130457, 1.32064172678175, 1.61378461617361, 1.77550053496517, 1.7444038412628, 1.88853641144745, 1.44554726136069, 1.52169806808946, 1.38482876983135, 1.50693746767208, 1.56467290333013, 1.55664813927757, 1.65044541980974, 1.50357779174674, 0.764054807030921, 0.495778285132959, 0.596411488294826, 0.693937433662632, 0.932011439769366, 1.10514738904478, 0.904927677550852, 0.969827490109055, 1.05101670841548, 1.12392075468322, 1.33509689588176, 1.20342821574007, 0.993967001310557, 1.20898145783709, 1.05447146612782, 0.744232955898758, 0.844458068128595, 0.780144262373508, 0.547168299748365, 2.83490197058459, 1.23839568773548, 1.10988758878693, 0.867733363063826, 0.73962833236251, 0.85416901457008, 0.784618799106827, 0.725934558423183, 0.834658973996715, 0.875948198454898, 0.881964176473614, 0.933974735332668, 0.870382237844421, 0.772181200080244, 0.829814527221943, 0.929018949289857, 0.892966747157662, 0.891484338497291, 0.942086202624782, 0.671953671900624, 0.788435092592626, 0.620007838797593, 0.87841213508972, 1.09789753017474, 0.917411139887858, 1.12120717286975)),
row.names = c(2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 129L, 130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 139L, 140L, 141L, 142L, 143L, 144L, 145L, 146L, 147L, 148L, 149L, 150L, 151L, 152L, 153L, 154L, 155L, 156L, 158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 176L, 177L, 178L, 179L, 180L, 181L, 182L, 183L, 184L, 185L, 186L, 187L, 188L, 189L, 190L, 191L, 192L, 193L, 194L, 195L, 196L, 197L, 198L, 199L, 200L, 201L, 202L),
class = "data.frame")
Option 1: Density Polygons
ggplot(DataS, aes(comp1, mod1obs)) +
stat_density_2d(aes(fill = ..level..),
geom = "polygon",
bins = 10) +
scale_fill_gradientn(colors = c("#FFEDA0",
"#FEB24C",
"#F03B20"))
Option 2: Hexagon Bins
ggplot(DataS, aes(comp1, mod1obs)) +
geom_hex(bins = 10) +
scale_fill_gradientn(colors = c("#FFEDA0",
"#FEB24C",
"#F03B20"))
Data
DataS <- structure(list(comp1 = c(1800.2451, 1781.6087, 1737.2914, 1816.9749, 1643.348, 1438.5377, 1338.2461, 1721.8119, 1878.2578, 1781.8827, 1813.2432, 1711.5277, 2033.0855, 1636.394, 1580.0748, 1834.4927, 2150.4177, 1790.7859, 1980.2718, 1610.0624, 2064.5809, 2002.82, 1652.7442, 2143.8216, 2015.1028, 2201.7947, 1610.4855, 1983.2706, 1979.9317, 2282.4141, 1763.288, 2204.3358, 1781.4969, 2114.2082, 1567.7841, 2089.6192, 1653.2401, 1709.9662, 2118.7251, 1843.5898, 1561.9472, 1839.0119, 2441.8013, 1684.3595, 1862.4287, 2043.3588, 2043.1502, 1999.5591, 1929.3686, 1897.746, 2073.494, 1345.488, 1622.3962, 1692.8681, 1845.1739, 1661.4121, 1746.5679, 2225.7098, 1973.0317, 1877.0764, 1875.4714, 1996.5981, 1712.4015, 1715.2255, 2149.4741, 2303.7732, 1872.9362, 2279.4588, 1513.787, 2184.5552, 2361.3593, 2148.0699, 1978.381, 2008.029, 1955.9995, 1986.7266, 2079.5143, 1960.9757, 1809.0558, 2193.2401, 1729.3818, 1926.0217, 2157.6266, 1788.1556, 1475.6945, 1854.1782, 2090.9429, 2242.7276, 2120.1378, 1982.7239, 1860.3072, 1906.2126, 2115.5587, 1970.3582, 1809.3253, 1851.249, 2147.1908, 1774.673, 1816.4041, 1934.3119, 1901.6345, 1943.6281, 1793.2585, 1826.9192, 1727.9485, 1506.61, 1440.656, 1748.6882, 1956.9693, 1938.5561, 1850.1536, 1645.5995, 2086.7712, 1723.6074, 1805.0789, 2089.9296, 2139.9376, 1891.8347, 2158.9863, 1525.1479, 2132.2023, 2061.2398, 1707.3251, 2202.9106, 2017.0934, 2359.064, 1781.5352, 2081.9002, 2091.391, 2307.257, 1722.3419, 2200.4204, 1752.5609, 2110.2504, 1432.5841, 2121.8681, 1734.5244, 1912.6964, 2134.543, 1996.4716, 1720.3715, 1902.6841, 2402.3462, 1850.2001, 1919.6724, 2123.1945, 2177.1916, 2134.0072, 1969.5795, 1896.8108, 2132.3515, 1451.0973, 1612.5966, 1725.3021, 1581.0971, 2347.5604, 2483.6352, 2035.8553, 2426.3861, 1655.1293, 2382.9981, 2468.8676, 2256.6275, 2087.2235, 2124.0978, 2068.831, 2150.4903, 2262.8365, 2198.5672, 1968.5931, 2418.6407, 1953.0811, 2108.4554, 2361.584, 1979.866, 1741.6303, 2087.9856, 2286.7883, 2395.2738, 2270.3263, 2075.8854, 1874.2124, 2038.3772, 2245.1034, 2075.8632, 1946.548, 1955.202, 2355.13, 2011.3717, 1975.8039, 2099.3788, 2136.0128, 1909.835, 1965.8319, 1829.9619, 1630.4157, 1506.5606, 1897.0851, 2069.4124, 2024.9848),
mod1obs = c(1.13466310792768, 0.931923716274403, 1.48217365869915, 1.21838260146353, 1.79940276419435, 1.76643559356371, 2.45574670199418, 1.85759003858763, 1.52414873587958, 1.56924844587154, 1.75782289196183, 1.21854647206407, 1.67402271470746, 1.57661741696957, 1.54915950383801, 1.16767311295364, 1.51513098169545, 1.58721974127532, 1.48203349385421, 1.25150055063036, 1.55523168986921, 1.16757976105911, 1.06103912158879, 1.38682784393209, 1.26706181742404, 0.922200575100749, 1.15475122512565, 0.919460864722599, 0.622581998594566, 0.888857360932288, 0.561258461668506, 0.784221352878807, 1.05841796267749, 0.954913917366984, 0.864989456398651, 0.405901971360923, 0.437238711188011, 0.238832040897009, 0.398444779604082, 0.596968751447861, 0.780373761619182, 0.593964337126505, 0.674617006647725, 0.803014994811282, 0.914435218814932, 0.832419908437008, 0.870618364283852, 0.66205782789832, 0.668271801470194, 0.582218329504303, 0.551471500972662, 0.546862377936455, 0.782666777798501, 0.797397188682925, 0.233703554128143, 0.33078183414865, 0.477622787992685, 0.788352242940992, 0.655192745268702, 0.629025187982574, 0.606750329361944, 0.688876228035905, 0.811952779578203, 0.878151955673824, 0.666102340741867, 0.234485500963779, 0.377020837708222, 0.431015138976773, 0.599545478659815, 0.485341173599891, 0.54118137004067, 0.822700963102403, 0.680465320458669, 0.750819741408091, 0.914425077741122, 1.03163581838662, 0.750374660256944, 0.994337592683161, 1.00941746422589, 1.07118881485789, 1.2413361662074, 0.862210731330022, 0.807945428753615, 1.01206355371074, 1.1466781183792, 1.04520757430783, 0.906546865827641, 0.802138329712516, 0.927446849752543, 0.909606938810288, 0.726203964235328, 0.63174380758208, 0.827670510708034, 0.796140009519362, 0.908331184943455, 1.24437229369026, 1.01038032593664, 1.20106590210506, 1.47001915216196, 1.4216087908593, 1.19649249116061, 1.28729733432818, 1.050493946874, 0.490028901207541, 0.472897051757391, 0.474117219731324, 0.85500755809614, 1.16164758242536, 1.3257510136531, 1.28575789166575, 1.63477173725642, 2.45069111586777, 2.50502453325512, 1.70995863697504, 2.4729188484128, 2.65244156137921, 2.52288384634894, 2.91133218222026, 2.80631798084833, 2.85068631154525, 2.50623525545215, 2.39950652546403, 2.49930350794263, 2.47640980454363, 2.5743114316923, 2.27093700546959, 1.9784591153946, 1.794185941834, 1.59685624413893, 1.21577767933037, 1.1993875662254, 0.905547156548879, 0.791419465032495, 1.5777113878502, 1.46727507228871, 1.03812304747397, 1.04471285876448, 0.89791346550258, 1.00531560652384, 1.03963310517253, 1.55611837868102, 1.32499035130457, 1.32064172678175, 1.61378461617361, 1.77550053496517, 1.7444038412628, 1.88853641144745, 1.44554726136069, 1.52169806808946, 1.38482876983135, 1.50693746767208, 1.56467290333013, 1.55664813927757, 1.65044541980974, 1.50357779174674, 0.764054807030921, 0.495778285132959, 0.596411488294826, 0.693937433662632, 0.932011439769366, 1.10514738904478, 0.904927677550852, 0.969827490109055, 1.05101670841548, 1.12392075468322, 1.33509689588176, 1.20342821574007, 0.993967001310557, 1.20898145783709, 1.05447146612782, 0.744232955898758, 0.844458068128595, 0.780144262373508, 0.547168299748365, 2.83490197058459, 1.23839568773548, 1.10988758878693, 0.867733363063826, 0.73962833236251, 0.85416901457008, 0.784618799106827, 0.725934558423183, 0.834658973996715, 0.875948198454898, 0.881964176473614, 0.933974735332668, 0.870382237844421, 0.772181200080244, 0.829814527221943, 0.929018949289857, 0.892966747157662, 0.891484338497291, 0.942086202624782, 0.671953671900624, 0.788435092592626, 0.620007838797593, 0.87841213508972, 1.09789753017474, 0.917411139887858, 1.12120717286975)),
row.names = c(2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 129L, 130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L, 138L, 139L, 140L, 141L, 142L, 143L, 144L, 145L, 146L, 147L, 148L, 149L, 150L, 151L, 152L, 153L, 154L, 155L, 156L, 158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 176L, 177L, 178L, 179L, 180L, 181L, 182L, 183L, 184L, 185L, 186L, 187L, 188L, 189L, 190L, 191L, 192L, 193L, 194L, 195L, 196L, 197L, 198L, 199L, 200L, 201L, 202L),
class = "data.frame")
Is there a way to show the numbers that correspond to a point on a circle? I read up on the text(xy) function but it is for scatter plots which this is not. The scripts is as follows and the image attached shows what the result is. I would like to identify the point in the plots. Any help rendered is appreciated! Thanks.
library (circular)
df<- read.csv("Direction.csv", header = TRUE)
df1 <- df [ which(df$Month==1 & df$Day>0 & df$Day <32) ,]
df2 <- df1[c(-1,-2,-3)]
df3<- lapply(df2, function(df2) circular(df2, units='degrees', template='geographics'))
dens<- lapply(df3, density.circular, bw =5)
par(mfrow=c(5,4), oma=c(2,1.3,2,2), mar=c(1.5,2,2,1), tcl=-0.2, mgp=c(0,1,0))
titles <- c("1000mb", "925mb", "850mb", "700mb", "600mb", "500mb", "400mb", "300mb",
"250mb", "200mb", "150mb", "100mb","70mb", "50mb", "30mb", "20mb", "10mb")
for(i in 1:17){
plot(mean(df3[[1]]), main = titles[1],)
print(mean(df3[[1]]))
print(var(df3[[1]]))
print(summary(df3[[1]]))
}
dput(df3[1])
structure(list(X1000mb = structure(c(86L, 130L, 75L, 59L, 56L,
69L, 139L, 358L, 98L, 175L, 322L, 17L, 336L, 46L, 137L, 1L, 2L,
102L, 225L, 121L, 179L, 291L, 325L, 317L, 321L, 349L, 28L, 38L,
36L, 117L, 144L, 73L, 121L, 135L, 131L, 127L, 139L, 167L, 298L,
213L, 37L, 33L, 71L, 120L, 156L, 14L, 51L, 92L, 168L, 332L, 24L,
71L, 128L, 98L, 104L, 86L, 155L, 5L, 281L, 342L, 356L, 346L,
210L, 186L, 199L, 133L, 191L, 282L, 139L, 168L, 158L, 154L, 117L,
149L, 162L, 157L, 192L, 175L, 197L, 171L, 184L, 305L, 70L, 169L,
207L, 8L, 72L, 134L, 160L, 135L, 154L, 149L, 161L, 182L, 259L,
173L, 205L, 331L, 112L, 26L, 129L, 137L, 120L, 136L, 156L, 327L,
332L, 349L, 16L, 28L, 42L, 352L, 94L, 149L, 153L, 183L, 183L,
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357L, 250L, 36L, 62L, 103L, 1L, 5L, 55L, 97L, 114L, 143L, 156L,
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167L, 90L, 174L, 205L, 173L, 208L, 197L, 217L, 246L, 151L, 161L,
119L, 128L, 159L, 232L, 198L, 227L, 175L, 213L, 220L, 226L, 171L,
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25L, 306L, 305L, 338L, 3L, 319L, 12L, 70L, 19L, 185L, 199L, 88L,
140L, 176L, 207L, 149L, 155L, 162L, 152L, 164L, 178L, 201L, 214L,
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133L, 24L, 115L, 162L, 173L, 245L, 147L, 105L, 81L, 75L, 75L,
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295L, 358L, 311L, 16L, 17L, 7L, 47L, 345L, 4L, 36L, 118L, 209L,
173L, 231L, 8L, 90L, 156L, 237L, 163L, 343L, 350L, 354L, 36L,
62L, 45L, 43L, 95L, 113L, 164L, 317L, 315L, 168L, 188L, 190L,
168L, 227L, 185L, 142L, 249L, 200L, 228L, 7L, 50L, 95L, 265L,
10L, 75L, 63L, 151L, 124L, 146L, 35L, 303L, 331L, 218L, 303L,
312L, 341L, 33L, 36L, 9L, 74L, 85L, 105L, 99L, 101L, 91L, 130L,
152L, 14L, 211L, 271L, 319L, 315L, 309L, 358L, 31L), circularp = structure(list(
type = "angles", units = "degrees", template = "geographics",
modulo = "asis", zero = 1.5707963267949, rotation = "clock"), .Names = c("type",
"units", "template", "modulo", "zero", "rotation")), class = c("circular",
"integer"))), .Names = "X1000mb")
It doesn't matter that it's not strictly a "scatterplot" . Now that you've set up an array of subplots, you can cycle thru them again, but this time using text() to place data at the desired location within each subplot. Roughly,
for (i in 1:17 ) text(x_loc[i],y_loc[i], some_text_vector[i])
Where you've "preloaded" the text strings and locations.